Search results

This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.

This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.

This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.

Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.

There is some evidence to suggest that the historical challenge associated with recruiting and retaining Black and Brown Science, Technology, Engineering and Math (STEM) collegians is tied to early their teaching and learning experiences in Mathematics. This paper describes an National Science Foundation (NSF) funded project (NSF #2151043) whose goal is to attract, prepare and retain math teachers of color in high need school districts ensure that those teachers remain in the field long enough to make a meaningful impact on the minds and hearts of BIPOC students who are often, extrinsically, and intrinsically, discouraged from pursuing careers in STEM professions.

This mixed-methods study, which began in the summer of 2023, seeks to recruit, prepare, support and retain nineteen (19) Black and Brown math teachers for two (2) high need urban school districts. The expectancy value theory will be used to explain the performance, persistence, and choices of the teachers, while grounded theory will be utilized to understand the impact of the intensive mentorship and wellness coaching that applied over the first year of their preservice preparation and subsequent in-service years.

Measures of project efficacy won’t begin until 2025 and as such there are no findings or implications to draw from for the study at this time.

The intention of this paper is to augment the body of knowledge on recruiting and retaining Black and Brown math teachers for urban schools where the need for quality STEM teachers is critical.